MétaCan
Menu
Back to cohort
Record W7116892170 · doi:10.1093/inthealth/ihaf154

Air pollution and lung cancer in India: an escalating public health crisis

2025· article· en· W7116892170 on OpenAlexaff
Tarun Kumar Suvvari, Nithya Arigapudi, Shreya Veggalam

Bibliographic record

VenueInternational Health · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsLung cancerAir pollutionPublic healthAir pollutantsPublic health policyLung disease

Abstract

fetched live from OpenAlex

Dear Editor, Lung cancer has long been considered a smoker’s disease. Yet in India, a strikingly different narrative is emerging: one driven not by tobacco, but by toxic air.1 With some of the world’s highest concentrations of fine particulate matter (PM₂.₅), India faces a silent but escalating cases of lung cancer due to air pollution.2 With ambient fine PM₂.₅ levels routinely exceeding 100 µg/m³ in many urban centres, the risk posed by air pollution as a carcinogen deserves urgent attention.1,2 Ambient air pollution is now classified by the International Agency for Research on Cancer as a Group 1 human carcinogen.3 Exposure to major ambient air pollutants shows a significant association with both the incidence and mortality of cancer, particularly lung cancer.4 While tobacco remains the leading cause of lung cancer, there is an emerging and troubling trend of increasing incidence among never-smokers, particularly in India, where exposure to ambient and household air pollution is pervasive. Noronha et al. reported that, in India, 40–50% of lung cancer cases occur in never-smokers, with the proportion rising to as high as 83% among Indian women, highlighting the growing contribution of air pollution and biomass fuel exposure to the country’s lung cancer burden.5 Estimates from the India State-Level Disease Burden Initiative Air Pollution Collaborators (GBD 2019) indicate that lung cancer accounted for 1.3% of all air pollution-attributable disability-adjusted life-years nationally, a seemingly modest fraction that nevertheless translates into a considerable absolute burden given India’s large population and high levels of both ambient and household particulate exposure.6 The economic burden attributable to air pollution in India was estimated at US$36.8 billion in 2019, of which lung cancer accounted for 1.2%, reflecting substantial productivity loss and treatment expenditure. Although this share is smaller than that of other respiratory conditions, its growth reflects India’s ongoing epidemiological transition and increasing exposure to ambient PM₂.₅.6 One striking finding is that, from 1990 to 2019, deaths attributable to household air pollution declined markedly (64.2%), whereas those linked to ambient PM₂.₅ pollution rose by 115.3%.6 So, the above studies highlight that air pollution is a key non-tobacco driver of India’s lung cancer epidemic. For clinicians and policymakers, the implications of this epidemiological shift are profound. Current low-dose CT screening guidelines (largely derived in high-income settings and focused on heavy smokers) fail to account for environmental exposures as primary risk factors.7 Consequently, the non-smoking population—particularly in highly polluted urban areas—is often excluded from screening, leading to the devastating diagnosis of advanced-stage lung cancer, where curative options are limited. In India, incorporating airborne-pollution exposure history into risk-stratification algorithms may help to identify high-risk individuals among never-smokers and prompt earlier detection. From a public health perspective, targeting ambient pollution is itself an onco-preventive strategy. Reductions in PM₂.₅ levels translate not only to fewer respiratory and cardiovascular deaths, but may also prevent cancers. The Indian Government’s National Clean Air Programme aims for a 20–30% reduction in PM₂.₅ by 2024–2025, but achieving this target remains a challenge given the scale of industrial, vehicular and agricultural emissions.8 So, surveillance programmes and cancer registries should increasingly incorporate geospatial pollution data. Collaborative research is needed to define dose–response curves, histological subtype associations (e.g. adenocarcinoma in never-smokers) and to assess whether pollution modifies treatment outcomes or prognosis. Mitigation of this crisis demands multisectoral action, that is, stricter emission standards, investment in clean energy and integration of air-quality health metrics into cancer-control programmes. Reducing air pollution is not only an environmental imperative, but also an onco-preventive intervention. In conclusion, lung cancer in India must no longer be viewed solely through the lens of tobacco. We are witnessing a hidden epidemic of lung cancer driven by air pollution, and overlooking it means ignoring a preventable cause of cancer. Each delay in clean-air action adds to the cancer registry of tomorrow. Tarun Kumar Suvvari (Conceptualization, Resources, Writing—original draft, Writing—review & editing), Nithya Arigapudi (Conceptualization, Resources, Writing—original draft, Writing—review & editing, Project Administration), and Shreya Veggalam (Resources, Writing—original draft, Writing—review & editing, Supervision). No funding was received. The authors declare no conflicts of interests. Not applicable. Not applicable. No AI or AI tools were used while drafting the manuscript. However, Grammarly tool was used to correct the grammar in the manuscript.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.408
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractno

Explore more

Same venueInternational HealthSame topicAir Quality and Health ImpactsFrench-language works237,207